Previous
geologic and seismic modeling studies of prograding carbonate-ramp outcrops
(Permian Abo Formation, Apache Canyon) present excellent analogs for subsurface
reservoirs of similar architecture in the Permian Basin and elsewhere.
Velocity data were measured on outcrops at 10-ft trace intervals for lithofacies
boundaries with significant impedance contrasts and were then sampled
at 1-ms in time. The model is characterized by sigmoid to oblique clinoforms
within the reservoir architecture in layered geometry in the host formation.
Seismically thick and thin beds, as well as multiple lithofacies with
wide-ranging rock properties, make for a strong heterogeneity. To clearly
understand the reservoir models interpreted from seismic data, we first
constructed synthetic seismic models of the outcrop and then conducted
neural net inversion for best recoverable impedance section. We analyzed
the quality of the interpreted reservoir model by comparing inversion
results with the original outcrop model. Parameters tested included (1)
seismic data density (trace interval), (2) seismic data frequency, (3)
noise level of seismic data, (4) neural-net-training data size, (5) accuracy
of training data sampling, and (6) impedance distribution of modeled lithofacies.
The study shows the necessity of acquiring higher frequency (>100 Hz)
seismic data for high-resolution (<5 m) reservoir models in general,
as well as increasing the trace interval and reducing the time sample
rate for better recovery of reservoir information based on available seismic
technology. Precautions should be taken in seismic inversion and interpretation
to avoid inadequate sampling, location error, and overestimation of seismic
resolution.